Handwritten Arabic Calligraphy Generation: A Systematic Literature Review

Abstract

Arabic calligraphy is famous for its distinct artistic style. It is written by skilled calligraphers to highlight the beauty of Arabic letters and represent its rich artistry. Due to the complexity of Arabic text compared to other languages' scripts, Arabic calligraphy writing demands a significant investment of time and effort, as well as the acquisition of high skills from calligraphers to correctly form the curves of Arabic script and accurately represent its various styles. This Systematic Literature Review (SLR) aims to provide a comprehensive analysis of the current state of research in Arabic calligraphy generation using deep learning and generative models. The review follows the PRISMA guidelines and examines 19 primary studies selected from a systematic search of academic databases, with publications spanning from January 2009 to December 2024. The findings indicate that Generative Adversarial Networks (GANs) and their variants are the most commonly used models for generating Arabic calligraphy. Additionally, the review highlights a significant gap in the availability of large, standardized handwritten datasets for model training and evaluation, as most existing datasets are small, custom-made, or privately held. In conclusion, the review offers valuable insights that can help researchers and practitioners advance the field, enabling the generation of high-quality Arabic calligraphy that satisfies both artistic and functional needs.